Titlebook: Linear Mixed Models for Longitudinal Data; Geert Verbeke,Geert Molenberghs Book 2000 Springer Science+Business Media New York 2000 Fitting

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Exploring Serial Correlation,ess, especially in the presence of random effects. This is because the residual variability is in practice very often dominated by the random effects in the model. In this chapter, we will discuss two procedures for exploring the residual covariance, conditionally on a set of random effects already

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Inelasticity

Conditional Linear Mixed Models,udinal studies, when compared to cross-sectional studies, is that they can distinguish changes over time within individuals (longitudinal effects) from differences among people in their baseline values (cross-sectional effects).

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Exploring Incomplete Data,vel standpoint (Figures 4.1 and 4.5) as well as from the population-averaged or group-averaged perspective (Figures 4.2, 4.3, 4.4, and 10.3). These plots are designed to focus on various structural aspects, such as the mean structure, the variance function, and the association structure.

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Joint Modeling of Measurements and Missingness,d measurements data. Missing data are indeed common in clinical trials (Piantadosi 1997, Green, Benedetti, and Crowley 1997, Friedman, Furberg, and DeMets 1998), in epidemiologic studies (Kahn and Sempos 1989, Clayton and Hills 1993, Lilienfeld and Stolley 1994, Selvin 1996), and, very prominently,

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Selection Models,layed a central role. This combines a marginal Gaussian regression model for the response, as might be used in the absence of missing data, with a Gaussian-based threshold model for the probability of a value being missing. For simplicity, consider a single Gaussian-distributed response variable ..